Instructions to use MetaIX/Alpaca-30B-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MetaIX/Alpaca-30B-Int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MetaIX/Alpaca-30B-Int4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MetaIX/Alpaca-30B-Int4") model = AutoModelForCausalLM.from_pretrained("MetaIX/Alpaca-30B-Int4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MetaIX/Alpaca-30B-Int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MetaIX/Alpaca-30B-Int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaIX/Alpaca-30B-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MetaIX/Alpaca-30B-Int4
- SGLang
How to use MetaIX/Alpaca-30B-Int4 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MetaIX/Alpaca-30B-Int4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaIX/Alpaca-30B-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "MetaIX/Alpaca-30B-Int4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MetaIX/Alpaca-30B-Int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MetaIX/Alpaca-30B-Int4 with Docker Model Runner:
docker model run hf.co/MetaIX/Alpaca-30B-Int4
Update README.md
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README.md
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@@ -10,10 +10,10 @@ This was made using Chansung's 30B Alpaca Lora: https://huggingface.co/chansung/
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<p><strong><font size="5">Benchmarks</font></strong></p>
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<strong>Wikitext2</strong>: 4.
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<strong>Ptb</strong>:
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<strong>C4</strong>: 6.
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<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
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<p><strong><font size="5">Benchmarks</font></strong></p>
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<strong>Wikitext2</strong>: 4.608365058898926
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<strong>Ptb-New</strong>: 8.69663143157959
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<strong>C4-New</strong>: 6.624773979187012
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<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
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